Upload app.py

app.py

@@ -11,16 +11,7 @@ from transformers import pipeline
 pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Small-hf")
 
 
-def apply_depth_aware_blur(
-    image,
-    foreground_blur,
-    midground_blur,
-    background_blur,
-    foreground_threshold,
-    midground_lower,
-    midground_upper,
-    background_threshold,
-):
+def apply_depth_aware_blur(image):  # Removed blur parameters
     original_image = Image.fromarray(image).convert("RGB")
     original_image = original_image.resize((512, 512))
     image_np = np.array(original_image)
@@ -33,70 +24,36 @@ def apply_depth_aware_blur(
     # Normalize the depth map
     normalized_depth_map = (depth - np.min(depth)) / (np.max(depth) - np.min(depth))
 
-    # Create masks
-    foreground_mask = (normalized_depth_map < foreground_threshold).astype(np.uint8) * 255
-    midground_mask = (
-        (normalized_depth_map >= midground_lower)
-        & (normalized_depth_map < midground_upper)
-    ).astype(np.uint8) * 255
-    background_mask = (normalized_depth_map >= background_threshold).astype(
-        np.uint8
-    ) * 255
-
     blurred_image = np.copy(np.array(original_image))
 
-    # Apply blur, ensuring kernel size is valid
-    if foreground_blur > 0 and foreground_blur % 2 == 1:
-        blurred_image = np.where(
-            (foreground_mask[..., None] == 255),
-            cv2.GaussianBlur(blurred_image, (foreground_blur, foreground_blur), 10),
-            blurred_image,
-        )
-    if midground_blur > 0 and midground_blur % 2 == 1:
-        blurred_image = np.where(
-            (midground_mask[..., None] == 255),
-            cv2.GaussianBlur(blurred_image, (midground_blur, midground_blur), 8),
-            blurred_image,
-        )
-    if background_blur > 0 and background_blur % 2 == 1:
-        blurred_image = np.where(
-            (background_mask[..., None] == 255),
-            cv2.GaussianBlur(blurred_image, (background_blur, background_blur), 20),
-            blurred_image,
-        )
+    # Calculate blur kernel size based on depth
+    max_kernel_size = 35  # Maximum blur kernel size
+    for y in range(512):
+        for x in range(512):
+            blur_amount = normalized_depth_map[y, x]
+            kernel_size = int(blur_amount * max_kernel_size)
+            # Ensure kernel size is odd and at least 1
+            kernel_size = max(1, kernel_size)
+            if kernel_size % 2 == 0:
+                kernel_size += 1
+
+            if kernel_size > 1 and kernel_size <= max_kernel_size:
+                blurred_image[y:y + 1, x:x + 1] = cv2.GaussianBlur(
+                    np.array(original_image)[y:y + 1, x:x + 1], (kernel_size, kernel_size), 10
+                )
+            else:
+                blurred_image[y:y + 1, x:x + 1] = np.array(original_image)[y:y + 1, x:x + 1] #Keep original pixel
 
-    return Image.fromarray(blurred_image.astype(np.uint8))
 
+    return Image.fromarray(blurred_image.astype(np.uint8))
 
-# Example input values (including defaults)
-example_image = np.zeros((512, 512, 3), dtype=np.uint8)  # Placeholder for an image
-example_inputs = [
-    example_image,
-    15,  # foreground_blur
-    7,  # midground_blur
-    35,  # background_blur (default)
-    0.2,  # foreground_threshold (default)
-    0.2,  # midground_lower (default)
-    0.6,  # midground_upper (default)
-    0.6,  # background_threshold (default)
-]
 
 iface = gr.Interface(
     fn=apply_depth_aware_blur,
-    inputs=[
-        gr.Image(label="Input Image"),
-        gr.Slider(1, 51, step=2, label="Foreground Blur Kernel Size"),
-        gr.Slider(1, 51, step=2, label="Midground Blur Kernel Size"),
-        gr.Slider(1, 51, step=2, label="Background Blur Kernel Size"),
-        gr.Slider(0, 1, label="Foreground Threshold"),
-        gr.Slider(0, 1, label="Midground Lower Threshold"),
-        gr.Slider(0, 1, label="Midground Upper Threshold"),
-        gr.Slider(0, 1, label="Background Threshold"),
-    ],
+    inputs=gr.Image(label="Input Image"),
     outputs=gr.Image(label="Blurred Image"),
-    title="Depth-Aware Lens Blur App",
-    description="Apply depth-based blur to uploaded images using Depth Anything V2. Adjust blur intensity for foreground, midground, and background.",
-    examples=[example_inputs],  # Provide example inputs
+    title="Depth-Proportional Lens Blur App",
+    description="Apply blur to an image where the blur intensity is proportional to the estimated depth. Farther objects are more blurred, closer objects are sharper.",
 )
 
 if __name__ == "__main__":